15305 results about "Road surface" patented technology

The invention belongs to the technical field of path planning, and discloses an unmanned driving dynamic path planning method and system based on multi-source data fusion, and the method comprises the steps: collecting an ice and snow pavement friction coefficient, a curve curvature and an obstacle point cloud, constructing a sensor confidence coefficient matrix, generating a fused semantic map, and constructing a dynamic environment semantic model. Outputting a real-time friction coefficient field and a risk thermodynamic map layer; the roadside unit broadcasts coordinates of opposite vehicles in a blind area of a curve to a vehicle end, constructs an ice and snow pavement offset crowdsourcing map, and generates a global-local fusion topology; fusing the real-time friction coefficient field and the global-local fusion topology to generate a smooth trajectory set, and further generating a risk optimal path instruction set; a steering angle and torque instruction is decomposed, positioning drift is compensated in real time, and a normal mode for updating the vehicle positioning state and a degradation mode when the millimeter wave radar fails are constructed; and generating execution logs and health state vectors, and aggregating the execution logs and the health state vectors of multiple vehicles to form closed-loop iterative update.

The invention discloses a mountainous area icy road vehicle driving speed early warning method based on physical information constraint, and relates to the technical field of intelligent traffic and vehicle safety, and the method comprises the steps: carrying out the multi-modal data collection and preprocessing, constructing a multi-modal feature extraction network to extract a high-dimensional vector, constructing a fusion module based on an attention mechanism to output a fusion feature vector, and carrying out the early warning of the driving speed of a mountainous area icy road. The method has the advantages that data such as environment, vehicle dynamics and road surface friction coefficients are collected through the multi-source sensor fusion technology, a multi-modal feature extraction and fusion network is constructed, and the real-time classification early warning is realized through the multi-modal feature extraction and fusion network; the method comprises environment, vehicle dynamic and road friction feature extraction sub-networks, deep extraction of different modal data features, a fusion network based on an attention mechanism, concerning of association, coupling and cooperative influence among data, and dynamic weighting of fusion features, so that the purposes of accurately sensing a complex environment, reducing false alarm and missing alarm and effectively improving early warning accuracy are achieved.

The invention discloses an intelligent pavement disease diagnosis method based on image recognition, and relates to the technical field of pavement disease diagnosis, and the method comprises the steps: deploying an image collection device and a pavement monitoring sensor in a target pavement region, so as to obtain multi-source pavement data; preprocessing the multi-source road surface data, and performing feature extraction to obtain a road surface feature sequence; and based on a deep learning algorithm and in combination with the pavement feature sequence, learning features of different disease types, constructing a disease identification classification model, and identifying different types of pavement diseases. Through the high-definition camera and the image processing technology, various disease types such as cracks, pit slots and ruts can be quickly and accurately identified, and in combination with a deep learning algorithm, disease features can be automatically extracted, high-precision identification of road diseases is realized, the disease detection efficiency is remarkably improved, manual intervention is reduced, and the detection efficiency is improved. And timely and accurate data support is provided for road maintenance.

The invention discloses a municipal road pavement crack multi-modal fusion detection method, which belongs to the technical field of pavement crack detection, and comprises the following steps: S1, obtaining visible light images, infrared thermal imaging and three-dimensional laser scanning data in multi-modal data, generating a time-unified and space-aligned multi-modal data set based on the three-dimensional laser scanning data; s2, extracting a visible light image and an infrared thermal image from the multi-modal data set, and obtaining features corresponding to the visible light image and the infrared thermal image to obtain a multi-modal feature set; and S3, inputting the multi-modal feature set into a deep neural network, and carrying out weighted integration on feature vectors through multi-layer convolution operation to obtain a crack detection result with three-dimensional coordinates. The municipal road pavement crack multi-modal fusion detection method solves the problem that an existing pavement crack detection method is low in accuracy and reliability.

The invention relates to the technical field of road engineering detection, in particular to a highway pavement crack image intelligent detection system and method, and the system comprises an image acquisition module, a deep learning module, an image enhancement module, a crack measurement and calculation module, a crack development trend prediction module and an information visualization module. The core innovation of the invention lies in that a differential geometry theory is introduced to construct a crack development trend prediction module, and the module comprises a crack characterization model based on a differential manifold, a multi-scale crack evolution tensor field analysis model and a nonlinear space-time crack development prediction model. A pavement is regarded as a two-dimensional differential manifold, cracks are represented as singular curves on the manifold, a multi-scale tensor field analysis technology and a non-linear kinetic equation are combined, accurate prediction of the future development trend of the cracks is achieved, the system supports multiple image acquisition modes, transverse, longitudinal and net cracks can be accurately detected, and the detection precision is high. The method adapts to complex illumination and background conditions, and predicts the expansion rate and severity change of the crack.

The invention provides an automatic target disease identification method and system based on cloud-edge collaboration, and the method comprises the steps: collecting an original road surface monitoring data set through an edge calculation node disposed in a road monitoring region, generating a multi-dimensional disease feature set in the edge calculation node, uploading the multi-dimensional disease feature set to a cloud analysis platform, and carrying out the recognition of a target disease through the cloud-edge collaboration. And performing feature matching degree calculation on the multi-dimensional disease feature set and a standard disease pattern in a cloud disease feature library through a cloud analysis platform, generating a target disease type identification result and a corresponding confidence coefficient evaluation parameter, and judging a result according to a relationship between the confidence coefficient evaluation parameter and a preset threshold value. And adjusting a feature extraction strategy of the edge computing node, generating an edge node adaptive optimization instruction set, and triggering feature extraction rule updating and disease recognition model parameter iteration operation for a subsequent monitoring period. According to the method, the disease identification accuracy is improved, and the real-time performance of edge calculation and the global optimization capability of cloud analysis are considered at the same time.

The invention discloses a pavement crack semantic segmentation method based on Transform and CNN architecture, and relates to the technical field of pavement crack detection, and the method comprises the steps: obtaining a pavement image, marking a crack disease image in the pavement image through an image marking tool, and constructing a data set based on the marked crack disease image; preprocessing the data set, and constructing and training a self-adaptive semantic segmentation model combining a self-attention neural network and a convolutional neural network based on the preprocessed data set; and deploying the trained adaptive semantic segmentation model to a local platform, and inputting the crack disease image into the adaptive semantic segmentation model for segmentation and marking. According to the method, a dynamic scale selector composed of a texture perceptron and a scale regulation and control unit is introduced, the size of a convolution kernel is dynamically adjusted according to the local texture complexity of an image, and fine modeling of different structure areas is achieved.

The invention provides a road surface defect detection method and system based on multi-sensor fusion, and the method comprises the steps: 1, capturing a road surface image through an RGB camera, and obtaining the three-dimensional point cloud data of a road surface through LiDAR; 2, performing feature extraction on the road surface image captured by the RGB camera by using a YOLOv5 network; step 3, processing the road surface three-dimensional point cloud data acquired by the LiDAR by using a Point Net + + network; step 4, carrying out deep fusion on the image features extracted by the YOLOv5 and the point cloud features extracted by the Point Net + +; and 5, detecting and classifying the road surface defects by adopting a quantitative and qualitative combined method. According to the method, an RGB camera and LiDAR (light detection and distance measurement) are adopted, a YOLOv5 network is combined with PointNet + +, high-precision and real-time detection and classification of road surface defects are achieved, and the defects of a traditional method are overcome.

The invention discloses an LED street lamp multi-scene intelligent regulation and control method and system for road illumination. The method comprises the steps that environment sensing parameters and equipment state parameters are collected; constructing a dynamic light scattering model according to the environment perception parameters and the equipment state parameters, predicting effective illumination distribution from the LED light source to the road surface in the current environment, and generating an LED spectrum compensation strategy; the real value of effective illumination distribution from the LED light source to the road surface is monitored in real time, the deviation between the real value and a predicted value is analyzed, and if the deviation exceeds a preset threshold value, the light attenuation coefficient, the road surface equivalent reflectivity and the LED working temperature are calculated in a reverse iteration mode according to the deviation so as to update the LED spectrum compensation strategy; and generating a PWM (Pulse Width Modulation) driving signal according to the updated LED spectrum compensation strategy, controlling the duty ratio of each sub light source in the LED array, and dynamically adjusting the LED spectrum distribution. According to the method, the LED operation parameters are dynamically regulated and controlled through the deviation between the true value and the predicted value of the effective illumination distribution, the light attenuation is compensated in real time, the spectral efficiency is optimized, and the LED power consumption is reduced on the premise of ensuring the illumination quality.

The invention discloses a construction monitoring system for a municipal asphalt road, relates to the technical field of road construction, and optimizes the road construction process through four modules of whole-process data acquisition, edge analysis and decision, dynamic quality detection and dynamic feedback control. And collecting data of each construction stage in real time. Real-time analysis is carried out through a BP multi-target decision network, the construction process is adjusted, quality detection is carried out on the surface layer and the deep layer of a road through a lightweight detection model, and defect marking and thermodynamic diagram display are carried out through a BIM platform. And on the basis of analysis and detection results, a fuzzy PID algorithm and a near-end strategy optimization algorithm are adopted, construction parameters are adjusted in real time, and construction quality and efficiency optimization are ensured. The problems that an existing sampling detection method is difficult to comprehensively cover a construction area, the quality problems of different levels cannot be visually displayed, and timely processing of constructors is affected are solved, and comprehensive monitoring, real-time analysis and intelligent adjustment of the road construction process can be achieved.

The invention provides a hot in-place recycling asphalt mixture and a construction method, and relates to the technical field of asphalt mixtures, and the hot in-place recycling asphalt mixture is obtained by recycling old asphalt pavement materials in a high proportion, reducing new material consumption and carbon emission, realizing resource recycling and green construction and introducing a regenerant to recover the performance of aged asphalt. The high-temperature rut resistance, the low-temperature crack resistance, the fatigue resistance and the interface bonding strength of the mixture are synergistically improved by matching with the styrene butadiene rubber modifier, the basalt fiber, the nano silicon dioxide and other functional materials, and the mixture is ensured to be uniform and free of segregation through precise feeding sequence, high-pressure atomization spraying and forced stirring parameter control. The defects of gray materials, oil balls and the like are avoided, the compactness, the flatness and the overall structure stability of the regenerated pavement are improved, and the problems of performance degradation and insufficient durability of a traditional regenerated mixture are solved.

The invention discloses a pavement crack detection method based on a Yolov8 model, and belongs to the technical field of crack detection. Comprising the following steps: constructing a pavement crack segmentation data set: acquiring a road crack image through an unmanned aerial vehicle, performing data enhancement processing of geometric transformation and color transformation on the image in combination with a public data set, performing Gaussian filtering denoising on a noise image, and performing crack labeling by using Label; based on a YOLOv8-Seg model, improvement is carried out by introducing a PKIblock multi-scale convolution kernel, generalizing an efficient layer aggregation network GELAN module, an EMA attention mechanism and replacing a spatial pyramid pooling layer SPPF into a SimSPPF, and a road crack recognition model YOLOv8-RCI is constructed; and performing crack detection and instance segmentation on the unmanned aerial vehicle image by using the trained YOLOv8-RCI model, and outputting a crack position and mask information. Through the improvement in the four aspects, the detection segmentation performance of the model is improved, and the model meets the requirement of real-time detection.

According to the new energy automobile brake pressure control method based on dynamic environment perception, a multi-window environment perception dynamic time warping algorithm is constructed, so that the similarity of different driver brake sequences is analyzed more meticulously; according to the method, a pavement adhesion coefficient observer for optimizing variable structure Kalman filtering based on a deep reinforcement learning algorithm is provided to calculate a pavement adhesion coefficient; secondly, a slip rate dynamic adjustment factor is obtained through calculation based on a wheel dynamics model, a vehicle density correction coefficient is obtained through calculation based on vehicle density, a road adhesion correction coefficient is obtained based on a road adhesion correction parameter constraint expression, and influences of different external environments on the road adhesion coefficient can be fully considered; the brake pressure calculation is more reasonable, and the driving safety is improved. According to the method, classification results of different drivers are combined, a comprehensive self-adaptive brake pressure calculation model is constructed to cope with different types of drivers, and corresponding final brake pressure is obtained.

The invention discloses a magneto-rheological suspension semi-active feedback control system based on laser radar and multi-sensor fusion. The magneto-rheological suspension semi-active feedback control system comprises a radar assembly, a sensor assembly and a feedback control assembly which are integrally installed on a vehicle body. Related road surface and vehicle driving data are obtained through the laser radar, the vehicle attitude sensor and the vehicle speed sensor, the collected data are analyzed and processed through the data processing unit, and real-time road condition scores and vehicle driving parameters are obtained; generating a suspension control signal according to the real-time road condition score and the vehicle driving parameters, and transmitting the suspension control signal to a suspension controller; the suspension controller transmits current to the magnetorheological dampers, and therefore the magnetorheological dampers are controlled to output damping force to the corresponding wheels. The response speed of the magneto-rheological suspension can be remarkably increased, the adaptability of the magneto-rheological suspension to complex road conditions is enhanced, and therefore the driving stability, controllability and riding comfort of a vehicle are remarkably improved.

The invention relates to a highway pavement crack real-time monitoring and early warning method and system. The method comprises the following steps: calculating a road surface stress change trend under traffic load and environment coupling through real-time environment monitoring data and historical traffic load data, and evaluating a potential risk level of crack expansion based on the trend; calculating an environment correction factor by combining the temperature change rate and the humidity change rate, and dynamically adjusting the risk level so as to predict the material durability attenuation and crack propagation dynamic change value; and generating a real-time evolution track by continuously updating the crack state data, and triggering a graded early warning instruction when the expansion rate exceeds an early warning threshold value. By adopting the method, multi-dimensional dynamic coupling analysis of load, environment and material performance can be realized, the accuracy and timeliness of crack propagation early warning are remarkably improved, false alarms and missing alarms are reduced, and a preventive maintenance decision of a highway pavement is optimized.

The invention relates to the technical field of road defect detection, in particular to a road pit detection method and system, and the method comprises the following steps: S1, obtaining an original image flow of a road surface through a vehicle-mounted multispectral camera, and carrying out the motion artifact elimination to generate a vibration compensation image; s2, generating a shadow suppression image through asymmetric gamma correction; s3, adopting a dual-threshold connected domain analysis method to extract candidate pothole regions; s4, when the contrast difference value exceeds a texture mutation threshold value, determining that the area is a surface damaged area; s5, extracting a continuous pixel cluster statistical area proportion, and when the proportion is greater than 60%, determining that the feature is an effective pothole feature; and S6, calculating the minimum enclosing ellipse eccentricity rate and the ellipse area ratio, and generating a final pothole detection report. According to the method, through a multi-source information fusion and multi-stage feature extraction method, high-precision identification and standardized output of the pothole area in a complex road environment are realized, and the detection accuracy and the application reliability are improved.

The invention, which relates to the technical field of highway traffic safety monitoring, discloses an expressway pavement ice condensation monitoring system comprising a data acquisition module for acquiring environmental data and pavement icing state information on an expressway pavement; and the self-calibration module is connected with the data acquisition module, performs self-calibration on the data acquisition module according to the environment data acquired in real time and the road surface icing state information, and corrects sensor errors caused by external environment factors. According to the highway pavement ice condensation monitoring system, a self-calibration function is added in monitoring equipment and an intelligent algorithm is introduced, so that the monitoring system can automatically correct measurement errors caused by external environment changes in real time, and a machine learning model is combined to predict pavement conditions more accurately; the working stability of the system in severe weather can be improved, and the risk of icing of the road surface can be found earlier, so that more accurate early warning information is provided for a traffic management department, and the probability of traffic accidents is reduced.

The invention discloses a highway pavement microcrack detection system based on an improved swarm algorithm, and the system comprises an image collection module which is used for outputting a standardized highway pavement image; the image preprocessing module is used for generating a preprocessed highway pavement image; the parameter optimization module is used for performing global search and local refinement search on the preprocessed highway pavement image based on a two-stage bitter fish optimization algorithm model; the image segmentation module is used for generating a preliminary micro-crack candidate region map; the feature extraction module is used for forming a depth feature map; the region reconstruction module is used for outputting a final highway pavement micro-crack region segmentation result; and the detection and output module is used for carrying out candidate region detection and positioning on the highway pavement micro-crack region segmentation result and outputting a final highway pavement micro-crack detection result containing the micro-crack position and region information. According to the method, the response capability to a weak edge is improved, a crack topological structure consistency function is fused in a local refining stage, and the detection structure reduction precision of a micro-crack region is improved.

The invention relates to the technical field of electric vehicle control, and discloses a stability control method and system for an electric vehicle, and the method comprises the steps: collecting vehicle driving data in real time, and dynamically estimating the state parameters of the vehicle through an unscented Kalman filtering algorithm; according to the state parameters of the vehicle, combined with the kinematic model and the foresight trajectory, the expected yawing moment in a short time in the future is calculated, and the longitudinal traction requirement of the vehicle is obtained; and according to the state parameters of the vehicle and the longitudinal traction requirement of the vehicle, the yawing moment and the traction force are decoupled and distributed to the four electric driving wheel ends, and stable control over the electric vehicle is achieved. Compared with a traditional stable control system only based on closed-loop feedback, the stable control system has the advantages that the control response is faster, the yawing intervention is more accurate, and better control performance and vehicle safety are shown under the low-adhesion road surface and high-load working conditions.

The invention discloses a pavement throwing detection method and device based on deep learning, electronic equipment and a program product. The method is realized through a trained detection model, the model adopts an LMSADet detection head, a multi-scale feature extraction and space attention mechanism is introduced into a task branch, and multi-scale modeling is decoupled from a backbone network and a neck network and integrated to the detection head so as to fit a detection task to directly optimize local details and scale differences of a throwing target. In order to suppress background interference and improve the recognition effect of fuzzy boundaries, the neck network is added into an MSHA module so as to efficiently capture the semantic relation between the thrown object and the background and enhance the regional understanding ability. A C3ESP module is introduced into the backbone network, deep features are extracted through stacking depth separable convolution, and information loss is avoided in combination with residual optimization fusion; meanwhile, a PEMA attention mechanism is introduced, the importance of different receptive field features is dynamically adjusted, the model focuses on key features, data information is captured more comprehensively, and therefore the detection performance is remarkably improved.

The invention relates to the technical field of measurement and monitoring of road engineering variables, in particular to an asphalt pavement paving quality management system, which comprises a data acquisition module configured to acquire real-time meteorological data, mixture temperature parameters and pavement quality monitoring data; the viscosity analysis module is used for determining the dynamic viscosity adjustment range of the asphalt material based on the real-time meteorological data and the temperature parameters of the mixture; the temperature control module is used for regulating and controlling the mixing temperature according to the viscosity change of the asphalt material and generating a target paving temperature curve; the power optimization module is used for matching a dynamic adjustment strategy of the heating power of the screed of the paver based on the target paving temperature curve; by means of the system, meteorological and construction data are monitored in real time, parameters such as asphalt viscosity and screed power are dynamically regulated and controlled, closed-loop optimization is achieved, the paving quality is improved, and intelligent construction is achieved.

Disclosed in the present invention are a water-reactive asphalt cold patch, a preparation method therefor and a use method thereof. The water-reactive asphalt cold patch of the present invention comprises the following components in parts by mass: 1000 parts of aggregates; 50-60 parts of mineral powder, 85-150 parts of emulsified asphalt, 15-30 parts of a demulsifier, and 1-10 parts of a super absorbent resin. The preparation method for the water-reactive asphalt cold patch of the present invention comprises the following steps: 1) adding aggregates to a mixing vessel and mixing uniformly; 2) adding emulsified asphalt to the mixing vessel and mixing uniformly; 3) adding mineral powder and a demulsifier to the mixing vessel and mixing uniformly; and 4) adding a super absorbent resin to the mixing vessel and mixing uniformly. The water-reactive asphalt cold patch of the present invention has the advantages of simple storage, convenient construction, rapid early strength development, good adhesion to the original pavement, strong resistance to water damage, and eco-friendliness, and is suitable for large-scale popularization and application.

The invention discloses a deep learning-based three-dimensional ground penetrating radar pavement damage multi-dimensional diagnosis method, which comprises the following steps: collecting and sorting three-dimensional ground penetrating radar road detection data, the data comprising N B-SCAN maps, M C-SCAN maps, road positions and point coordinates, and preprocessing the B-SCAN maps and the C-SCAN maps; based on a YOLO v12 model, obtaining a first identification result for the preprocessed B-SCAN atlas; obtaining a second identification result based on the preprocessed C-SCAN map; the first recognition result and the second recognition result are further judged through the optimized multi-dimensional cross recognition rule to determine the final recognition result, in the diagnosis method, the recognition model is optimized, and the recall rate and the accuracy rate of the mode in graph recognition are improved to the maximum extent; and the false abnormal signals are further screened, so that the model is improved, the recognition precision is improved, the multi-dimensional cross recognition rule is optimized, and the omission ratio and the accuracy of abnormal defects are reduced.

The invention discloses a pavement collapse risk monitoring method and system, and the method comprises the steps: carrying out the cross-modal fusion of a visible light image and an infrared image, and generating a pavement image; inputting the pavement image into a lightweight convolutional neural network to extract multi-scale image features, and when the multi-scale image features meet a preset condition, updating the initial collapse factor set based on feature distribution divergence difference in a preset sliding time window to obtain a target collapse factor set; constructing a multi-modal knowledge graph based on the collapse factor set, the historical collapse data and the historical environmental data, performing hierarchical causal reasoning on the multi-modal knowledge graph through a gated space-time diagram convolutional network to obtain a first collapse feature, and determining a second collapse feature based on the first collapse feature and the multi-source environmental data; and inputting the multi-scale image features and the second collapse features into a reinforcement learning model to determine a road collapse risk level. According to the invention, the accuracy and real-time performance of pavement collapse risk monitoring can be improved.

The invention relates to the technical field of automobile brake control, in particular to a self-adaptive brake kinetic energy recovery control method and system based on multi-sensor fusion. Tire wear and road surface feature data are collected through a multi-source sensing fusion unit, and a tire wear coefficient and a road surface friction coefficient are generated through an intelligent decision calculation unit; the method comprises the following steps of: integrating the nonlinear correlation of the multi-source sensing fusion unit and the self-adaptive control execution unit through a rule and data fusion algorithm, outputting a comprehensive friction coefficient, and dynamically adjusting the braking kinetic energy recovery force and response time by the self-adaptive control execution unit according to the comprehensive friction coefficient. The system comprises a multi-source sensing fusion unit, an intelligent decision calculation unit, the self-adaptive control execution unit and a closed-loop feedback calibration unit. The closed-loop unit corrects data deviation through cross validation of the laser radar and the motor torque inversion model, the problems of poor working condition adaptation and unreliable data are solved, and the kinetic energy recovery efficiency, the braking safety and the driving smoothness are improved.

The invention discloses a vehicle stability adaptive control method under low attachment road surface multi-source interference, and belongs to the field of vehicle control, and the method comprises the following steps: S1, multi-source interference perception and attachment coefficient prediction; s2, dynamic correction of tire dynamic parameters: correcting tire cornering stiffness and slippage stiffness on line; s3, multi-source interference collaborative observation and estimation: dynamically estimating an interference source by using an enhanced extended state observer; s4, phase plane stability envelope analysis and weight adjustment: dynamically allocating control priorities; and S5, on the premise of considering an interference source, performing dynamic torque distribution and actuator cooperative control: optimizing four-wheel torque by using a quadratic programming algorithm to obtain a torque distribution result. By adopting the vehicle stability adaptive control method under the low-attached road surface multi-source interference, the problems of insufficient modeling precision, weak interference suppression, target conflict and redundancy deficiency in low-attached road surface vehicle stability control are solved, and the trajectory tracking precision and the yaw stability are remarkably improved.

The invention relates to the technical field of road repair state monitoring, in particular to a road repair state monitoring method and system based on edge calculation. The method comprises the following steps: acquiring planning information of a repaired road, deploying 3D laser scanning vibration meters at a plurality of repairing points, collecting pavement vibration signals in different time periods, and performing disordered distribution time domain feature analysis to obtain a vibration signal disordered distribution time domain graph; then, structure weakening behavior simulation is carried out based on the graph, periodic structure weakening data is quantified, and a pavement water damage cumulative gradient is obtained through water damage cumulative gradient estimation; and finally, in combination with the periodic structure weakening data and the water damage cumulative gradient, carrying out service life evaluation on the restoration state, and sending an evaluation result to the terminal. According to the invention, the road repair state monitoring technology is optimized, so that the road repair state monitoring technology is more perfect.

The invention relates to an urban updated unmanned asphalt pavement compaction system and an intelligent control construction method. The unmanned asphalt pavement compaction system comprises an unmanned road roller, a server, infrared temperature acquisition equipment, environment temperature detection equipment, steel wheel temperature monitoring equipment, compaction degree monitoring equipment, vehicle-mounted laser radar equipment and a control and signal processing module. Multi-source sensing of a construction site is achieved, it is guaranteed that the pavement compaction quality can be monitored, and on the basis of multi-source sensing data obtained in real time, running and working parameters of an unmanned road roller are dynamically adjusted through the intelligent control construction method of the urban updating unmanned asphalt pavement compaction system, so that the pavement compaction quality is improved. The adaptability of asphalt pavement compaction of the unmanned road roller under different working conditions is improved, and the construction quality is further guaranteed.

Disclosed is a corner module apparatus for a vehicle. The corner module includes a driving unit that provides drive power to a wheel of the vehicle, a braking unit that interferes with rotation of the wheel to generate braking power, and a suspension unit connected to the driving unit. The suspension unit absorbs shock transferred from a road surface. The corner module also includes a steering unit connected to the suspension unit and rotated about a steering axis inclined with respect to the wheel. The steering unit adjusts a steering angle of the wheel.

The invention relates to an automobile seat adjusting method and device, a vehicle and a storage medium, and the method comprises the steps that multi-dimensional biomechanical data including body pressure distribution data of a driver and passengers and deep muscle group activity spectrum information are obtained; the method comprises the following steps: collecting motion state data and pavement environment data of an automobile, generating an automobile passenger environment coupling data set according to the motion state data and the pavement environment data of the automobile, and establishing a driving environment dynamic parameter library of the automobile according to the automobile passenger environment coupling data set; and based on the multi-dimensional biomechanical data and parameters in the driving environment dynamic parameter library, using a preset digital twin support model to determine control parameters of the automobile, generating an adjusting instruction of the automobile seat according to the control parameters, and adjusting the automobile seat according to the at least one seat adjusting action. Therefore, the problems that in the prior art, the intelligent degree and the individuation degree of seat adjustment are low, and continuous and comfortable sitting and riding experience is difficult to provide for a driver and passengers are solved.